This competition is provided as a way to explore different time series techniques on a relatively simple and clean dataset. Your task is to forecast demand for four beach front restaurants with a variety of items for sale. 

You are given 2.75 years of store-item sales data, and asked to predict 3 months of sales for 10 different items at 5 different restaurants. Your data set contains both structured and unstructured elements.

What is the best way to deal with seasonality? Should items be modeled separately, or can they be combined? Should you choose deep learning or ARIMA? Can either beat XGBoost?

This competition allows you to explore different models and improve your skills in forecasting.

![About the Data](https://data.heatonresearch.com/images/wustl/kaggle/demand_forecast/introduce_data.png)

## Similar Kaggles

* [Store Item Demand Forecasting Challenge](https://www.kaggle.com/c/demand-forecasting-kernels-only)

# Where the data came from
This data was created using a multi-agent simulation.

* Location: Ft. Lauderdale, FL, USA.
* Data from Jan 1, 2019 to Dec 31, 2021
* Dates only specify seasonality
* No black swan events: no pandemics, wars, market conditions, etc.
Holidays matter.
* Data set contains "trends" and multiple seasonalities.